晚上1点，OpenAI的直播如约而至。 其实在预告的时候，几乎已经等于明示了。 这块大概解释一下，别看底下模型那么多，乱七八糟，各种变体。 但是从最早的o1到如今的o3和o4‑mini，核心差别就在于模型规模、推理能力和插件工具的接入。 没有废话，今天发布的就是o3和o4-mini。 但是奥特曼这个老骗子，之前明明说o3不打算单独发布要融到GPT-5里面一起发，结果今天又发了。。。 ChatGPT Plus、Pro和Team用户从今天开始将在模型选择器中看到o3、o4-mini和o4-mini-high，取代o1、o3-mini和o3-mini-high。 我的已经变了，但是我最想要的o3 pro，还要几周才能提供，就很可惜，现在o1 pro被折叠到了更多模型里。 说实话纯粹的模型参数的进步，其实已经没啥可说的了，这次最让我觉得最大的进步点，是两个： 1. 满血版的o3终于可以使用工具了。 2. o3和o4-mini 是o系列中最新的视觉推理模型，第一次能够在思维链中思考图像了。 照例，我一个一个来说，尽可能给大家一个，非常全面完整的总结。 一.o3和o4-mini性能 其实没有特别多的意思，就跟现在数码圈一 ...

编辑 | 硬 AI OpenAI最新模型取得突破性进展：具备原创构思能力。 点击 上方 硬AI 关注我们 据介绍，最新模型不仅能总结研究论文或解决数学问题，还能够独立提出新构思，连接不同领域的概念，提出创新性实验 设计，完成需要科学家跨领域合作才能实现的成果，相当于"博士水平AI"。 硬·AI 作者 | 李笑寅 据媒体援引知情人士消息， OpenAI最早将在本周发布代号为o3或o4-mini的新模型， 该模型不仅能总结 研究论文或解决数学问题，还能够独立提出新构思，连接不同领域的概念，提出创新性实验设计。 据介绍，即将推出的新模型能同时利用物理学、工程学和生物学等多个领域的知识，提供跨学科的解决方 案，而科学家通常需要跨领域合作才能实现类似成果，相当"博士水平AI"。 硬·AI OpenAI总裁Greg Brockman在2月的"AI研讨会"活动上曾表示： "我们真正的方向是开发能够花大量时间认真思考重要科学问题的模型，我希望在未来几年内，这将 使所有人的效率提高10倍或100倍。" * 感谢阅读！ * 转载、合作、交流请留言，线索、数据、商业合作请加微信：IngAI2023 * 欢迎大家在留言区分享您的看法 ...

Investment Rating - The report maintains a "Buy-A" rating for China Mobile (600941.SH) [1] Core Views - In 2024, China Mobile achieved revenue of 1,040.8 billion yuan, a year-on-year increase of 3.1%, with a net profit of 138.4 billion yuan, up 5.0% year-on-year [2][3] - The company is focusing on new business areas such as 5.5G, AI, and reasoning computing, which are expected to drive future growth [10] - The report anticipates steady growth in the company's overall business, with a projected net profit of 147.8 billion yuan in 2025, reflecting a growth rate of 6.8% [12] Summary by Sections Personal Market - As of December 31, 2024, the total number of mobile customers reached 1.004 billion, with 5G network customers increasing to 552 million, a penetration rate of 55.0% [3] - The personal mobile cloud revenue grew by 12.6% year-on-year to 8.9 billion yuan, while the mobile ARPU decreased slightly by 0.8 yuan to 48.5 yuan [3][4] Family Market - The number of broadband customers reached 315 million, with gigabit broadband customers increasing by 25.0% to 99 million [4] - The family customer ARPU increased by 0.7 yuan to 43.8 yuan, indicating a positive trend in the family market [4] Business and New Markets - The enterprise market revenue reached 209.1 billion yuan, up 8.8% year-on-year, with a significant increase in mobile cloud revenue, which surpassed 100 billion yuan, growing by 20.4% [6][9] - Emerging markets, including international business and digital content, saw revenue growth of 8.7% year-on-year, with international business revenue at 22.8 billion yuan [9] Capital Expenditure - Capital expenditure for 2024 was 164 billion yuan, a decrease of 9.0% year-on-year, with a focus on new areas such as 5.5G and AI [10] - The company plans to invest 98 billion yuan in 5G-A in 2025, reflecting a significant increase of 227% [10] Dividend Policy - The company plans to increase its dividend payout, with a total dividend of 5.09 HKD per share for 2024, a year-on-year increase of 5.4% [11] Financial Forecasts - The report forecasts net profits of 147.8 billion yuan, 156.2 billion yuan, and 164.5 billion yuan for 2025, 2026, and 2027 respectively, with corresponding EPS of 6.85, 7.24, and 7.62 yuan [12][14]

金磊 梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 没错，里面也可以是英特尔的 锐炫 显卡！ 那这性能到底能不能跟上呢？ 带着这个问题，在体验之余，我们还"抓"来了一个正在为这种一体机开发方案的软件公司进行了一番"盘问"。 这家公司叫 飞致云 ，主要是把大模型一体机用在了自家的 MaxKB 上（一款基于大语言模型的知识库问答系统）。 他们是把4张锐炫 A770显卡和2张N卡放到一起，做了一下对比测试： 对于同一个相对规模较大的任务，搭载N卡的一体机大约耗时半小时，而搭载锐炫 A770显卡的一体机则需要50分钟。 家人们，你知道近段时间大火的各种大模型 一体机 ，里面到底是什么卡吗？ 相信很多小伙伴的第一反应，或许就是N卡。 但在我们接触、体验了真实的大模型一体机之后，发现了一个大写的 "万万没想到" ： 但是！ 买半张N卡的钱 ，就能轻松搞定4张锐炫 显卡。 由此，飞致云给出了这样一个结论： 基于锐炫 A770显卡的大模型一体机， 在性价比上真的是太香了 。 它非常适合30-50人规模的团队来使用。 一个"性价比"关键词，道破了为什么大模型一体机里面会出现英特尔游戏卡。 毕竟之前企业要私有化部署一个目 ...

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容编译自 nextplatform ，谢谢。 2023 年，迈克·亨利 (Mike Henry) 担任AI 推理公司 Groq 的临时首席产品官，这一职位使他与许 多数据中心管理员和经理保持密切联系。在这六个月中，他注意到不断变化的格局发生了变化，而 主导云服务提供商的领域一直是亚马逊网络服务(AWS)、微软 Azure 和谷歌云平台。 虽然这些超大规模企业继续占据人工智能领域的大量空间，但亨利看到越来越多的 GPU 云提供商涌 入市场，建立了配备数千个Nvidia 芯片的数据中心，这些芯片正在推动推理和其他人工智能工作负 载所需的计算。 "我意识到，现在大多数人工智能基础设施都是在三大传统云提供商之外构建的，"亨利告诉The Next Platform。"我生活在一个超级扩张者总是获胜的世界里，我看到了这一巨大的变化和巨大的机 遇。" Heny 和自动驾驶汽车公司 Swift Navigation 的联合创始人兼首席执行官 Tim Harris 于 2023 年底 利用这个机会创立了 Parasail。Parasail 本周凭借 1000 万美元的种子资金和 ...

随着我们迈入 Agentic 时代，对于各组织机构而言，若要对模型进行扩展以实现高效推理，他们将需要 在从训练到推理的每一个步骤中都遵循扩展流程。在 NVIDIA GTC 2025 上，黄仁勋的愿景以及所发布 的消息聚焦于在从企业信息技术、云计算到机器人技术等各个行业中构建 " AI工厂"。 为了让AI工厂取得成功，NVIDIA持续创新，并提供完整的AI技术栈，包括芯片、系统和软件，以最高 的效率来加速和扩展AI。该公司的方法涵盖了Agentic AI 和 Physical AI 领域。NVIDIA在其整个技术栈 方面做出了以下发布内容： 图片来源：NVIDIA & Counterpoint Research 芯片方面：从计算路线图到硅光子学领域 都有重大消息发布 图片来源：NVIDIA NVIDIA 的芯片产品组合涵盖了中央处理器（CPU）、图形处理器（GPU）以及网络设备（用于纵 向扩展和横向扩展）。 NVIDIA 发布了其最新的 " Blackwell超级AI工厂" 平台 GB300 NVL72，与 GB200 NVL72 相比，其 AI性能提升了 1.5 倍。 NVIDIA 分享了其芯片路线图，这样一 ...

Core Viewpoint - The article discusses the competitive landscape between Zhipu and DeepSeek, highlighting Zhipu's recent product launches and pricing strategies aimed at challenging DeepSeek's dominance in the AI model market [2][10]. Product Launches - On March 31, Zhipu launched the "AutoGLM Thinking Model" and the inference model "GLM-Z1-Air," claiming that Air can match the performance of DeepSeek's R1 model with only 32 billion parameters compared to R1's 671 billion parameters [2]. - The pricing for Zhipu's model is set at 0.5 yuan per million tokens, significantly lower than DeepSeek's pricing, which is 1/30 of DeepSeek's model [2]. Market Dynamics - The article notes a shift in the AI model industry, with some companies, including Baichuan Intelligence and Lingyi Wanyi, experiencing strategic pivots or downsizing, indicating a loss of investor patience with AI startups [3][4]. - Despite the challenges, Zhipu continues to secure funding from state-owned enterprises, positioning itself as a leader among the "six small tigers" in the large model sector [4][6]. Commercialization Challenges - The commercialization of large models remains a significant hurdle for the industry, with Zhipu acknowledging the need to pave the way for an IPO while facing uncertain market conditions [6]. - Zhipu is focusing on penetrating various sectors, including finance, education, healthcare, and government, while also establishing an alliance with ASEAN countries and Belt and Road nations for collaborative model development [6]. Strategic Positioning - Zhipu's CEO emphasizes the company's commitment to pre-training models, despite industry trends moving towards post-training and inference models [3][12]. - The company aims to balance its technological advancements with commercial strategies, ensuring that both aspects support each other dynamically [21]. Future Outlook - The article suggests that Zhipu is optimistic about achieving significant growth in 2025, with expectations of a tenfold increase in market opportunities, while maintaining a stable commercialization strategy [22].

Core Insights - The competition in the AI large model sector is shifting towards AI inference, marking the beginning of the AI inference era, with edge computing emerging as a new battleground in this field [1][2]. AI Inference Era - Major tech companies have been active in the AI inference space since last year, with OpenAI launching the O1 inference model, Anthropic introducing the "Computer Use" agent feature, and DeepSeek's R1 inference model gaining global attention [2]. - NVIDIA showcased its first inference model and software at the GTC conference, indicating a clear shift in focus towards AI inference capabilities [2][4]. Demand for AI Inference - According to a Barclays report, the demand for AI inference computing is expected to rise rapidly, potentially accounting for over 70% of the total computing demand for general artificial intelligence, surpassing training computing needs by 4.5 times [4]. - NVIDIA's founder Jensen Huang predicts that the computational power required for inference could exceed last year's estimates by 100 times [4]. Challenges and Solutions in AI Model Deployment - Prior to DeepSeek's introduction, deploying and training AI large models faced challenges such as high capital requirements and the need for extensive computational resources, making it difficult for small and medium enterprises to develop their own ecosystems [4]. - DeepSeek's approach utilizes large-scale cross-node expert parallelism and reinforcement learning to reduce reliance on manual input and data deficiencies, while its open-source model significantly lowers deployment costs to the range of hundreds of calories per thousand calories [4]. Advantages of Edge Computing - AI inference requires low latency and proximity to end-users, making edge or edge cloud environments advantageous for running workloads [5]. - Edge computing enhances data interaction and AI inference efficiency while ensuring information security, as it is geographically closer to users [5][6]. Market Competition and Player Strategies - The AI inference market is rapidly evolving, with key competitors including AI hardware manufacturers, model developers, and AI service providers focusing on edge computing [7]. - Companies like Apple and Qualcomm are developing edge AI chips for applications in AI smartphones and robotics, while Intel and Alibaba Cloud are offering edge AI inference solutions to enhance speed and efficiency [7][8]. Case Study: Wangsu Technology - Wangsu Technology, a leading player in edge computing, has been exploring this field since 2011 and has established a comprehensive layout from resources to applications [8]. - With nearly 3,000 global nodes and abundant GPU resources, Wangsu can significantly improve model interaction efficiency by 2 to 3 times [8]. - The company's edge AI platform has been applied across various industries, including healthcare and media, demonstrating the potential for AI inference to drive innovation and efficiency [8].

如果您希望可以时常见面，欢迎标星收藏哦~ 来源：内容编译自intel，谢谢。 以下为信件译文： 尊敬的股东： 本月初，当我接任英特尔首席执行官一职时，我以务实的态度关注业务，对公司抱有坚定的信念。 虽然我们需要克服一些明显的挑战，但也存在着加速扭转局面和提高业绩的重要机会。 我知道，要取得英特尔能够取得的成果，首先要重新关注我们的客户。从我上任第一天起，这一直 是首要任务。我正在认真听取他们的反馈意见，以便我们继续推动必要的变革，以取悦客户并加强 我们的竞争地位。 作为长期关注英特尔的人，我亲眼目睹，当公司提供让客户满意的出色产品时，它总是表现最佳。 这就是我作为领导者的心态。当我与我们的团队会面时，我看到了重振英特尔产品组合的机会，这 令我深受启发。 早前，英特尔任命陈立武为CEO。于是，大家对其如何拯救这家芯片巨头，充满好奇。在最新的一 封给股东的信中，我们可以看到这位巨头领导人新策略的蜘丝马迹。 简单来说，空谈的时代已经结束。我们必须把我们的话语付诸行动，兑现我们的承诺。我很高兴看 到领导团队已经开始推动实现这一目标所需的文化变革。作为首席执行官，我将继续推动这一转 变，以便我们行动更快、工作更聪明，让 ...

点击 上方 硬AI 关注我们 巴克莱指出，2025年AI行业有足够的算力来支持15亿到220亿个AI Agent。AI行业需从"无意义基准测试"转向实用的Agent产品部署，低推理成本是盈利关键，开源模型将降低 成本。尽管算力看似充足，但高效、低成本Agent产品的专用算力仍有缺口。 硬·AI 作者 |鲍亦龙 编辑 | 硬 AI 继TD Cowen后，巴克莱似乎也开始唱空AI算力。 3月26日，巴克莱发布最新研究称，2025年全球AI算力可支持15-220亿个AI Agent，这足以满足美国和欧盟1亿多白领工作者和超过10亿企业软件许可证的 需求。而同日 TD Cowen分析师称支撑人工智能运算的计算机集群供过于求 。 巴克莱认为现有的AI算力已经足够支持大规模AI代理的部署，主要基于以下三点： 行业推理容量基础 ：2025年全球约有1570万个AI加速器（GPU/TPU/ASIC等）在线，其中40%（约630万个）将用于推理， 而这些推理算力中约一半（310万个）将专门用于 Agent/聊天机器人服务 ； 可支持大量用户 ：根据不同模型的计算需求，现有算力可支持15亿到220亿个AI代理，这足以满足美国和欧 ...